Mining User Interests from User Search by Using Web Log Data

Abstract

Web Usage Mining (WUM) is a kind of data mining method that can be used to discover user access patterns from Web log data. A lot of work has been done already about this area and the obtained results are used in different applications such as recommending the Web usage patterns, personalization, system improvement and business intelligence. WUM includes three phases that are called preprocessing, pattern discovery and pattern analysis. There square measure totally different techniques for WUM that have their own benefits and downsides. We tend to initial describe a way for extracting a worldwide linguistics illustration of a pursuit question log then show, however, we are able to use it to semantically extract the user interests. During this paper extraction of users interest from journal knowledge will be done, that square measure supported visit time and visit density which might be get from an analysis of internet users journal knowledge.